Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows50009
Duplicate rows (%)5.0%
Total size in memory491.5 MiB
Average record size in memory515.4 B

Variable types

Categorical4
Text1
DateTime2
Numeric7

Alerts

Dataset has 50009 (5.0%) duplicate rowsDuplicates
Item Type is highly overall correlated with Unit Cost and 1 other fieldsHigh correlation
Total Cost is highly overall correlated with Total Profit and 4 other fieldsHigh correlation
Total Profit is highly overall correlated with Total Cost and 4 other fieldsHigh correlation
Total Revenue is highly overall correlated with Total Cost and 4 other fieldsHigh correlation
Unit Cost is highly overall correlated with Item Type and 4 other fieldsHigh correlation
Unit Price is highly overall correlated with Item Type and 4 other fieldsHigh correlation
Units Sold is highly overall correlated with Total Cost and 2 other fieldsHigh correlation

Reproduction

Analysis started2025-03-16 08:53:33.808812
Analysis finished2025-03-16 08:55:03.202577
Duration1 minute and 29.39 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

Region
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.5 MiB
Sub-Saharan Africa
259953 
Europe
259036 
Asia
146017 
Middle East and North Africa
124344 
Central America and the Caribbean
108042 
Other values (2)
102608 

Length

Max length33
Median length28
Mean length15.845056
Min length4

Characters and Unicode

Total characters15845056
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSub-Saharan Africa
2nd rowMiddle East and North Africa
3rd rowAustralia and Oceania
4th rowSub-Saharan Africa
5th rowEurope

Common Values

ValueCountFrequency (%)
Sub-Saharan Africa 259953
26.0%
Europe 259036
25.9%
Asia 146017
14.6%
Middle East and North Africa 124344
12.4%
Central America and the Caribbean 108042
10.8%
Australia and Oceania 80837
 
8.1%
North America 21771
 
2.2%

Length

2025-03-16T14:25:03.611215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-16T14:25:03.847417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
africa 384297
16.2%
and 313223
13.2%
sub-saharan 259953
11.0%
europe 259036
10.9%
north 146115
 
6.2%
asia 146017
 
6.2%
america 129813
 
5.5%
middle 124344
 
5.2%
east 124344
 
5.2%
central 108042
 
4.6%
Other values (4) 377758
15.9%

Most occurring characters

ValueCountFrequency (%)
a 2525027
15.9%
r 1476135
 
9.3%
1372942
 
8.7%
i 1054187
 
6.7%
e 918156
 
5.8%
n 870097
 
5.5%
A 740964
 
4.7%
u 599826
 
3.8%
c 594947
 
3.8%
t 567380
 
3.6%
Other values (16) 5125395
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15845056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2525027
15.9%
r 1476135
 
9.3%
1372942
 
8.7%
i 1054187
 
6.7%
e 918156
 
5.8%
n 870097
 
5.5%
A 740964
 
4.7%
u 599826
 
3.8%
c 594947
 
3.8%
t 567380
 
3.6%
Other values (16) 5125395
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15845056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2525027
15.9%
r 1476135
 
9.3%
1372942
 
8.7%
i 1054187
 
6.7%
e 918156
 
5.8%
n 870097
 
5.5%
A 740964
 
4.7%
u 599826
 
3.8%
c 594947
 
3.8%
t 567380
 
3.6%
Other values (16) 5125395
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15845056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2525027
15.9%
r 1476135
 
9.3%
1372942
 
8.7%
i 1054187
 
6.7%
e 918156
 
5.8%
n 870097
 
5.5%
A 740964
 
4.7%
u 599826
 
3.8%
c 594947
 
3.8%
t 567380
 
3.6%
Other values (16) 5125395
32.3%
Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.9 MiB
2025-03-16T14:25:04.617279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length22
Mean length8.90437
Min length4

Characters and Unicode

Total characters8904370
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Africa
2nd rowMorocco
3rd rowPapua New Guinea
4th rowDjibouti
5th rowSlovakia
ValueCountFrequency (%)
and 32435
 
2.4%
republic 27164
 
2.1%
the 27042
 
2.0%
of 21727
 
1.6%
saint 16247
 
1.2%
guinea 16235
 
1.2%
south 16216
 
1.2%
united 16202
 
1.2%
states 10901
 
0.8%
new 10893
 
0.8%
Other values (205) 1129873
85.3%
2025-03-16T14:25:05.413498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1346140
15.1%
i 773466
 
8.7%
n 702943
 
7.9%
e 622961
 
7.0%
o 486201
 
5.5%
r 470532
 
5.3%
t 367724
 
4.1%
362809
 
4.1%
u 312437
 
3.5%
l 292114
 
3.3%
Other values (43) 3167043
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8904370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1346140
15.1%
i 773466
 
8.7%
n 702943
 
7.9%
e 622961
 
7.0%
o 486201
 
5.5%
r 470532
 
5.3%
t 367724
 
4.1%
362809
 
4.1%
u 312437
 
3.5%
l 292114
 
3.3%
Other values (43) 3167043
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8904370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1346140
15.1%
i 773466
 
8.7%
n 702943
 
7.9%
e 622961
 
7.0%
o 486201
 
5.5%
r 470532
 
5.3%
t 367724
 
4.1%
362809
 
4.1%
u 312437
 
3.5%
l 292114
 
3.3%
Other values (43) 3167043
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8904370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1346140
15.1%
i 773466
 
8.7%
n 702943
 
7.9%
e 622961
 
7.0%
o 486201
 
5.5%
r 470532
 
5.3%
t 367724
 
4.1%
362809
 
4.1%
u 312437
 
3.5%
l 292114
 
3.3%
Other values (43) 3167043
35.6%

Item Type
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.5 MiB
Fruits
83551 
Personal Care
83539 
Snacks
83448 
Cosmetics
83431 
Baby Food
83397 
Other values (7)
582634 

Length

Max length15
Median length13
Mean length8.583558
Min length4

Characters and Unicode

Total characters8583558
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFruits
2nd rowClothes
3rd rowMeat
4th rowClothes
5th rowBeverages

Common Values

ValueCountFrequency (%)
Fruits 83551
8.4%
Personal Care 83539
8.4%
Snacks 83448
8.3%
Cosmetics 83431
8.3%
Baby Food 83397
8.3%
Beverages 83326
8.3%
Household 83267
8.3%
Clothes 83240
8.3%
Office Supplies 83222
8.3%
Cereal 83211
8.3%
Other values (2) 166368
16.6%

Length

2025-03-16T14:25:05.555244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fruits 83551
 
6.7%
personal 83539
 
6.7%
care 83539
 
6.7%
snacks 83448
 
6.7%
cosmetics 83431
 
6.7%
baby 83397
 
6.7%
food 83397
 
6.7%
beverages 83326
 
6.7%
household 83267
 
6.7%
clothes 83240
 
6.7%
Other values (5) 416023
33.3%

Most occurring characters

ValueCountFrequency (%)
e 1332568
15.5%
s 833625
 
9.7%
a 666828
 
7.8%
o 583538
 
6.8%
l 499649
 
5.8%
r 417166
 
4.9%
t 416590
 
4.9%
i 333426
 
3.9%
C 333421
 
3.9%
250158
 
2.9%
Other values (21) 2916589
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8583558
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1332568
15.5%
s 833625
 
9.7%
a 666828
 
7.8%
o 583538
 
6.8%
l 499649
 
5.8%
r 417166
 
4.9%
t 416590
 
4.9%
i 333426
 
3.9%
C 333421
 
3.9%
250158
 
2.9%
Other values (21) 2916589
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8583558
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1332568
15.5%
s 833625
 
9.7%
a 666828
 
7.8%
o 583538
 
6.8%
l 499649
 
5.8%
r 417166
 
4.9%
t 416590
 
4.9%
i 333426
 
3.9%
C 333421
 
3.9%
250158
 
2.9%
Other values (21) 2916589
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8583558
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1332568
15.5%
s 833625
 
9.7%
a 666828
 
7.8%
o 583538
 
6.8%
l 499649
 
5.8%
r 417166
 
4.9%
t 416590
 
4.9%
i 333426
 
3.9%
C 333421
 
3.9%
250158
 
2.9%
Other values (21) 2916589
34.0%

Sales Channel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.6 MiB
Offline
500249 
Online
499751 

Length

Max length7
Median length7
Mean length6.500249
Min length6

Characters and Unicode

Total characters6500249
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffline
2nd rowOnline
3rd rowOffline
4th rowOffline
5th rowOffline

Common Values

ValueCountFrequency (%)
Offline 500249
50.0%
Online 499751
50.0%

Length

2025-03-16T14:25:05.700051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-16T14:25:05.827524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
offline 500249
50.0%
online 499751
50.0%

Most occurring characters

ValueCountFrequency (%)
n 1499751
23.1%
f 1000498
15.4%
O 1000000
15.4%
l 1000000
15.4%
i 1000000
15.4%
e 1000000
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6500249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1499751
23.1%
f 1000498
15.4%
O 1000000
15.4%
l 1000000
15.4%
i 1000000
15.4%
e 1000000
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6500249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1499751
23.1%
f 1000498
15.4%
O 1000000
15.4%
l 1000000
15.4%
i 1000000
15.4%
e 1000000
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6500249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1499751
23.1%
f 1000498
15.4%
O 1000000
15.4%
l 1000000
15.4%
i 1000000
15.4%
e 1000000
15.4%

Order Priority
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
C
250313 
L
250133 
H
249861 
M
249693 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowH
5th rowL

Common Values

ValueCountFrequency (%)
C 250313
25.0%
L 250133
25.0%
H 249861
25.0%
M 249693
25.0%

Length

2025-03-16T14:25:06.107020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-16T14:25:06.293898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
c 250313
25.0%
l 250133
25.0%
h 249861
25.0%
m 249693
25.0%

Most occurring characters

ValueCountFrequency (%)
C 250313
25.0%
L 250133
25.0%
H 249861
25.0%
M 249693
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 250313
25.0%
L 250133
25.0%
H 249861
25.0%
M 249693
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 250313
25.0%
L 250133
25.0%
H 249861
25.0%
M 249693
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 250313
25.0%
L 250133
25.0%
H 249861
25.0%
M 249693
25.0%
Distinct2767
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Minimum2010-01-01 00:00:00
Maximum2017-12-07 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-16T14:25:06.562681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:25:06.830658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Order ID
Real number (ℝ)

Distinct900000
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4935202 × 108
Minimum1.0000118 × 108
Maximum9.9999989 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-03-16T14:25:07.138199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0000118 × 108
5-th percentile1.4472066 × 108
Q13.2396295 × 108
median5.4865235 × 108
Q37.745981 × 108
95-th percentile9.5535643 × 108
Maximum9.9999989 × 108
Range8.9999871 × 108
Interquartile range (IQR)4.5063515 × 108

Descriptive statistics

Standard deviation2.599397 × 108
Coefficient of variation (CV)0.47317511
Kurtosis-1.2001336
Mean5.4935202 × 108
Median Absolute Deviation (MAD)2.252914 × 108
Skewness0.004494625
Sum5.4935202 × 1014
Variance6.7568648 × 1016
MonotonicityNot monotonic
2025-03-16T14:25:07.390104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
673456180 2
 
< 0.1%
370419776 2
 
< 0.1%
847769296 2
 
< 0.1%
474296367 2
 
< 0.1%
667237317 2
 
< 0.1%
214140975 2
 
< 0.1%
222868669 2
 
< 0.1%
971594226 2
 
< 0.1%
558803975 2
 
< 0.1%
753296744 2
 
< 0.1%
Other values (899990) 999980
> 99.9%
ValueCountFrequency (%)
100001180 1
< 0.1%
100002467 1
< 0.1%
100002896 1
< 0.1%
100005042 1
< 0.1%
100007617 1
< 0.1%
100008904 1
< 0.1%
100009763 1
< 0.1%
100010192 1
< 0.1%
100011050 1
< 0.1%
100012767 1
< 0.1%
ValueCountFrequency (%)
999999892 1
< 0.1%
999999463 1
< 0.1%
999998605 1
< 0.1%
999996888 1
< 0.1%
999996459 1
< 0.1%
999996030 2
< 0.1%
999993884 1
< 0.1%
999992597 1
< 0.1%
999992167 2
< 0.1%
999991738 2
< 0.1%
Distinct2817
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Minimum2010-01-01 00:00:00
Maximum2017-09-17 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-16T14:25:07.641867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:25:07.896208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Units Sold
Real number (ℝ)

High correlation 

Distinct10000
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4998.8673
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-03-16T14:25:08.179786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile499
Q12502
median4998
Q37496
95-th percentile9497
Maximum10000
Range9999
Interquartile range (IQR)4994

Descriptive statistics

Standard deviation2885.3341
Coefficient of variation (CV)0.57719759
Kurtosis-1.1990497
Mean4998.8673
Median Absolute Deviation (MAD)2497
Skewness-0.00025107145
Sum4.9988673 × 109
Variance8325153.1
MonotonicityNot monotonic
2025-03-16T14:25:08.575228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8959 130
 
< 0.1%
7228 129
 
< 0.1%
187 129
 
< 0.1%
5170 129
 
< 0.1%
1826 129
 
< 0.1%
3258 129
 
< 0.1%
7632 128
 
< 0.1%
9019 128
 
< 0.1%
9764 128
 
< 0.1%
3065 128
 
< 0.1%
Other values (9990) 998713
99.9%
ValueCountFrequency (%)
1 101
< 0.1%
2 100
< 0.1%
3 93
< 0.1%
4 104
< 0.1%
5 94
< 0.1%
6 100
< 0.1%
7 114
< 0.1%
8 94
< 0.1%
9 111
< 0.1%
10 97
< 0.1%
ValueCountFrequency (%)
10000 95
< 0.1%
9999 107
< 0.1%
9998 92
< 0.1%
9997 100
< 0.1%
9996 92
< 0.1%
9995 99
< 0.1%
9994 107
< 0.1%
9993 110
< 0.1%
9992 89
< 0.1%
9991 103
< 0.1%

Unit Price
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.02549
Minimum9.33
Maximum668.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-03-16T14:25:08.803593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.33
5-th percentile9.33
Q181.73
median154.06
Q3421.89
95-th percentile668.27
Maximum668.27
Range658.94
Interquartile range (IQR)340.16

Descriptive statistics

Standard deviation216.98797
Coefficient of variation (CV)0.81566607
Kurtosis-0.80766595
Mean266.02549
Median Absolute Deviation (MAD)101.22
Skewness0.73655188
Sum2.6602549 × 108
Variance47083.777
MonotonicityNot monotonic
2025-03-16T14:25:09.044998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9.33 83551
8.4%
81.73 83539
8.4%
152.58 83448
8.3%
437.2 83431
8.3%
255.28 83397
8.3%
47.45 83326
8.3%
668.27 83267
8.3%
109.28 83240
8.3%
651.21 83222
8.3%
205.7 83211
8.3%
Other values (2) 166368
16.6%
ValueCountFrequency (%)
9.33 83551
8.4%
47.45 83326
8.3%
81.73 83539
8.4%
109.28 83240
8.3%
152.58 83448
8.3%
154.06 83170
8.3%
205.7 83211
8.3%
255.28 83397
8.3%
421.89 83198
8.3%
437.2 83431
8.3%
ValueCountFrequency (%)
668.27 83267
8.3%
651.21 83222
8.3%
437.2 83431
8.3%
421.89 83198
8.3%
255.28 83397
8.3%
205.7 83211
8.3%
154.06 83170
8.3%
152.58 83448
8.3%
109.28 83240
8.3%
81.73 83539
8.4%

Unit Cost
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.52298
Minimum6.92
Maximum524.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-03-16T14:25:09.622205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.92
5-th percentile6.92
Q135.84
median97.44
Q3263.33
95-th percentile524.96
Maximum524.96
Range518.04
Interquartile range (IQR)227.49

Descriptive statistics

Standard deviation175.6508
Coefficient of variation (CV)0.93668947
Kurtosis-0.68364978
Mean187.52298
Median Absolute Deviation (MAD)61.98
Skewness0.89459132
Sum1.8752298 × 108
Variance30853.203
MonotonicityNot monotonic
2025-03-16T14:25:09.773934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6.92 83551
8.4%
56.67 83539
8.4%
97.44 83448
8.3%
263.33 83431
8.3%
159.42 83397
8.3%
31.79 83326
8.3%
502.54 83267
8.3%
35.84 83240
8.3%
524.96 83222
8.3%
117.11 83211
8.3%
Other values (2) 166368
16.6%
ValueCountFrequency (%)
6.92 83551
8.4%
31.79 83326
8.3%
35.84 83240
8.3%
56.67 83539
8.4%
90.93 83170
8.3%
97.44 83448
8.3%
117.11 83211
8.3%
159.42 83397
8.3%
263.33 83431
8.3%
364.69 83198
8.3%
ValueCountFrequency (%)
524.96 83222
8.3%
502.54 83267
8.3%
364.69 83198
8.3%
263.33 83431
8.3%
159.42 83397
8.3%
117.11 83211
8.3%
97.44 83448
8.3%
90.93 83170
8.3%
56.67 83539
8.4%
35.84 83240
8.3%

Total Revenue
Real number (ℝ)

High correlation 

Distinct119901
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1329562.6
Minimum9.33
Maximum6682700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-03-16T14:25:09.996175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.33
5-th percentile33476.04
Q1277867.2
median784444.54
Q31822443.9
95-th percentile4607053.4
Maximum6682700
Range6682690.7
Interquartile range (IQR)1544576.7

Descriptive statistics

Standard deviation1468527.3
Coefficient of variation (CV)1.1045191
Kurtosis1.9386254
Mean1329562.6
Median Absolute Deviation (MAD)623339.46
Skewness1.5797682
Sum1.3295626 × 1012
Variance2.1565725 × 1012
MonotonicityNot monotonic
2025-03-16T14:25:10.249993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2990356.32 24
 
< 0.1%
808606.7 23
 
< 0.1%
152835.1 23
 
< 0.1%
69844.38 21
 
< 0.1%
1868972.7 21
 
< 0.1%
368449.25 21
 
< 0.1%
1738744.4 20
 
< 0.1%
1854602.4 20
 
< 0.1%
414902.8 20
 
< 0.1%
960207.6 20
 
< 0.1%
Other values (119891) 999787
> 99.9%
ValueCountFrequency (%)
9.33 13
< 0.1%
18.66 14
< 0.1%
27.99 9
< 0.1%
37.32 8
< 0.1%
46.65 9
< 0.1%
47.45 10
< 0.1%
55.98 13
< 0.1%
65.31 16
< 0.1%
74.64 5
 
< 0.1%
81.73 5
 
< 0.1%
ValueCountFrequency (%)
6682700 8
< 0.1%
6682031.73 7
< 0.1%
6681363.46 6
< 0.1%
6680695.19 6
< 0.1%
6680026.92 9
< 0.1%
6679358.65 10
< 0.1%
6678690.38 10
< 0.1%
6678022.11 11
< 0.1%
6677353.84 4
 
< 0.1%
6676685.57 7
< 0.1%

Total Cost
Real number (ℝ)

High correlation 

Distinct119734
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean937267.09
Minimum6.92
Maximum5249600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-03-16T14:25:10.507910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.92
5-th percentile22400
Q1161728.91
median466781.76
Q31196327.1
95-th percentile3612249.8
Maximum5249600
Range5249593.1
Interquartile range (IQR)1034598.2

Descriptive statistics

Standard deviation1148954.3
Coefficient of variation (CV)1.2258558
Kurtosis2.4179065
Mean937267.09
Median Absolute Deviation (MAD)387535.96
Skewness1.7495362
Sum9.3726709 × 1011
Variance1.3200959 × 1012
MonotonicityNot monotonic
2025-03-16T14:25:10.741559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24801.28 28
 
< 0.1%
305571.84 27
 
< 0.1%
98167.52 26
 
< 0.1%
18600.96 26
 
< 0.1%
230737.92 26
 
< 0.1%
243210.24 26
 
< 0.1%
46556.16 25
 
< 0.1%
118487.04 25
 
< 0.1%
456377.67 25
 
< 0.1%
87306.24 24
 
< 0.1%
Other values (119724) 999742
> 99.9%
ValueCountFrequency (%)
6.92 13
< 0.1%
13.84 14
< 0.1%
20.76 9
< 0.1%
27.68 8
< 0.1%
31.79 10
< 0.1%
34.6 9
< 0.1%
35.84 7
< 0.1%
41.52 13
< 0.1%
48.44 16
< 0.1%
55.36 5
 
< 0.1%
ValueCountFrequency (%)
5249600 10
< 0.1%
5249075.04 8
< 0.1%
5248550.08 10
< 0.1%
5248025.12 9
< 0.1%
5247500.16 12
< 0.1%
5246975.2 9
< 0.1%
5246450.24 7
< 0.1%
5245925.28 8
< 0.1%
5245400.32 4
 
< 0.1%
5244875.36 11
< 0.1%

Total Profit
Real number (ℝ)

High correlation 

Distinct119779
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean392295.56
Minimum2.41
Maximum1738700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-03-16T14:25:10.993481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.41
5-th percentile9570.11
Q195104.8
median281054.88
Q3565307.6
95-th percentile1217050
Maximum1738700
Range1738697.6
Interquartile range (IQR)470202.8

Descriptive statistics

Standard deviation378819.9
Coefficient of variation (CV)0.96564922
Kurtosis1.2660055
Mean392295.56
Median Absolute Deviation (MAD)218301.12
Skewness1.2949991
Sum3.9229556 × 1011
Variance1.4350452 × 1011
MonotonicityNot monotonic
2025-03-16T14:25:11.281419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66022.56 25
 
< 0.1%
105019.2 24
 
< 0.1%
14391.54 23
 
< 0.1%
433736.64 23
 
< 0.1%
472439.52 23
 
< 0.1%
12078.92 23
 
< 0.1%
86349.24 23
 
< 0.1%
849223.74 23
 
< 0.1%
140564.16 22
 
< 0.1%
48984.48 22
 
< 0.1%
Other values (119769) 999769
> 99.9%
ValueCountFrequency (%)
2.41 13
< 0.1%
4.82 14
< 0.1%
7.23 9
< 0.1%
9.64 8
< 0.1%
12.05 9
< 0.1%
14.46 13
< 0.1%
15.66 10
< 0.1%
16.87 16
< 0.1%
19.28 5
 
< 0.1%
21.69 6
 
< 0.1%
ValueCountFrequency (%)
1738700 10
< 0.1%
1738526.13 6
< 0.1%
1738352.26 8
< 0.1%
1738178.39 4
 
< 0.1%
1738004.52 4
 
< 0.1%
1737830.65 3
 
< 0.1%
1737656.78 8
< 0.1%
1737482.91 12
< 0.1%
1737309.04 8
< 0.1%
1737135.17 6
< 0.1%

Interactions

2025-03-16T14:24:51.344616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:20.559993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:28.438887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:33.652025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:38.124299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:43.971195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:47.539459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:51.909847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:21.201428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:29.208933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:34.169149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:38.844008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:44.464069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:48.060059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:52.476379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:22.664048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:30.590764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:34.660758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:40.118061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:44.971024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:48.594328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:53.116601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:24.427581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:31.386187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:35.236977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:40.784093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:45.479259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:49.119349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:53.744127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:25.651660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:31.949484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:35.767497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:42.174324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:46.033562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:49.627423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:54.308656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:26.421492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:32.440308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:36.229166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:42.841823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:46.524811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:50.210438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:54.694651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:27.275608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:33.104270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:37.497227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:43.470019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:46.992283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T14:24:50.786637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-16T14:25:11.506936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Item TypeOrder IDOrder PriorityRegionSales ChannelTotal CostTotal ProfitTotal RevenueUnit CostUnit PriceUnits Sold
Item Type1.0000.0000.0000.0000.0000.3550.3220.3341.0001.0000.000
Order ID0.0001.0000.0000.0000.000-0.000-0.000-0.000-0.000-0.000-0.000
Order Priority0.0000.0001.0000.0000.0010.0010.0000.0000.0000.0000.000
Region0.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.001
Sales Channel0.0000.0000.0010.0001.0000.0010.0020.0030.0000.0000.000
Total Cost0.355-0.0000.0010.0000.0011.0000.8840.9880.7930.7780.536
Total Profit0.322-0.0000.0000.0000.0020.8841.0000.9400.6000.6550.621
Total Revenue0.334-0.0000.0000.0000.0030.9880.9401.0000.7520.7590.573
Unit Cost1.000-0.0000.0000.0000.0000.7930.6000.7521.0000.972-0.001
Unit Price1.000-0.0000.0000.0000.0000.7780.6550.7590.9721.000-0.001
Units Sold0.000-0.0000.0000.0010.0000.5360.6210.573-0.001-0.0011.000

Missing values

2025-03-16T14:24:55.783667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-16T14:24:57.600530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RegionCountryItem TypeSales ChannelOrder PriorityOrder DateOrder IDShip DateUnits SoldUnit PriceUnit CostTotal RevenueTotal CostTotal Profit
0Sub-Saharan AfricaSouth AfricaFruitsOfflineM27-07-20124433689957/28/201215939.336.9214862.6911023.563839.13
1Middle East and North AfricaMoroccoClothesOnlineM14-09-201366759351410/19/20134611109.2835.84503890.08165258.24338631.84
2Australia and OceaniaPapua New GuineaMeatOfflineM15-05-201594099558506-04-2015360421.89364.69151880.40131288.4020592.00
3Sub-Saharan AfricaDjiboutiClothesOfflineH17-05-201788081153607-02-2017562109.2835.8461415.3620142.0841273.28
4EuropeSlovakiaBeveragesOfflineL26-10-201617459019412-04-2016397347.4531.79188518.85126301.6762217.18
5AsiaSri LankaFruitsOnlineL07-11-201183019288712/18/201113799.336.9212866.079542.683323.39
6Sub-Saharan AfricaSeychellesBeveragesOnlineM18-01-20134257934452/16/201359747.4531.7928327.6518978.639349.02
7Sub-Saharan AfricaTanzaniaBeveragesOnlineL30-11-20166598781941/16/2017147647.4531.7970036.2046922.0423114.16
8Sub-Saharan AfricaGhanaOffice SuppliesOnlineL23-03-20176012459634/15/2017896651.21524.96583484.16470364.16113120.00
9Sub-Saharan AfricaTanzaniaCosmeticsOfflineL23-05-20167390080805/24/20167768437.20263.333396169.602045547.441350622.16
RegionCountryItem TypeSales ChannelOrder PriorityOrder DateOrder IDShip DateUnits SoldUnit PriceUnit CostTotal RevenueTotal CostTotal Profit
999990Sub-Saharan AfricaZimbabweMeatOnlineC23-02-20104972801083/23/20105090421.89364.692147420.101856272.10291148.00
999991EuropeNetherlandsFruitsOfflineC28-08-201427762950609-05-201455959.336.9252201.3538717.4013483.95
999992Middle East and North AfricaIranPersonal CareOnlineH27-08-20134613556749/28/2013425181.7356.67347434.23240904.17106530.06
999993EuropeArmeniaPersonal CareOfflineC28-03-20154125699404/26/2015746881.7356.67610359.64423211.56187148.08
999994EuropeGermanyOffice SuppliesOnlineC16-03-201746569613204-04-20178689651.21524.965658363.694561377.441096986.25
999995Sub-Saharan AfricaSenegalBaby FoodOfflineL06-11-201057547057812-11-20103387255.28159.42864633.36539955.54324677.82
999996Central America and the CaribbeanPanamaOffice SuppliesOfflineC12-01-201576694210703-01-20154068651.21524.962649122.282135537.28513585.00
999997EuropeNorwayOffice SuppliesOnlineM25-10-201168547204712-05-20115266651.21524.963429271.862764439.36664832.50
999998EuropeMontenegroBeveragesOfflineM31-10-201094673422512-08-2010855147.4531.79405744.95271836.29133908.66
999999Central America and the CaribbeanNicaraguaMeatOnlineC17-03-201588671497104-08-20157519421.89364.693172190.912742104.11430086.80

Duplicate rows

Most frequently occurring

RegionCountryItem TypeSales ChannelOrder PriorityOrder DateOrder IDShip DateUnits SoldUnit PriceUnit CostTotal RevenueTotal CostTotal Profit# duplicates
0AsiaBangladeshBaby FoodOfflineH12-05-20118318674445/31/20116917255.28159.421765771.761102708.14663063.622
1AsiaBangladeshBaby FoodOfflineH20-03-20104110885024/17/20109078255.28159.422317431.841447214.76870217.082
2AsiaBangladeshBaby FoodOfflineH23-04-201177979482406-02-20117472255.28159.421907452.161191186.24716265.922
3AsiaBangladeshBaby FoodOfflineL22-06-201459701322307-04-20148334255.28159.422127503.521328606.28798897.242
4AsiaBangladeshBaby FoodOfflineL24-11-201112210429912/18/20112230255.28159.42569274.40355506.60213767.802
5AsiaBangladeshBaby FoodOfflineM05-02-201484774955502-06-20146787255.28159.421732585.361081983.54650601.822
6AsiaBangladeshBaby FoodOfflineM12-04-201543236850505-02-20156304255.28159.421609285.121004983.68604301.442
7AsiaBangladeshBaby FoodOfflineM16-03-20149518947953/26/20145678255.28159.421449479.84905186.76544293.082
8AsiaBangladeshBaby FoodOfflineM30-03-201536852849705-12-20154627255.28159.421181180.56737636.34443544.222
9AsiaBangladeshBaby FoodOnlineC03-04-20126957605714/14/20129869255.28159.422519358.321573315.98946042.342